L4DC 2024

139 papers

$\widetilde{O}(T^{-1})$ Convergence to (coarse) Correlated Equilibria in Full-Information General-Sum Markov Games Weichao Mao, Haoran Qiu, Chen Wang, Hubertus Franke, Zbigniew Kalbarczyk, Tamer Başar

A Data-Driven Riccati Equation Anders Rantzer

A Deep Learning Approach for Distributed Aggregative Optimization with Users’ Feedback Riccardo Brumali, Guido Carnevale, Giuseppe Notarstefano

A Framework for Evaluating Human Driver Models Using Neuroimaging Christopher Strong, Kaylene Stocking, Jingqi Li, Tianjiao Zhang, Jack Gallant, Claire Tomlin

A Large Deviations Perspective on Policy Gradient Algorithms Wouter Jongeneel, Daniel Kuhn, Mengmeng Li

A Learning-Based Framework to Adapt Legged Robots On-the-Fly to Unexpected Disturbances Nolan Fey, He Li, Nicholas Adrian, Patrick Wensing, Michael Lemmon

A Multi-Modal Distributed Learning Algorithm in Reproducing Kernel Hilbert Spaces Aneesh Raghavan, Karl Henrik Johansson

Adapting Image-Based RL Policies via Predicted Rewards Weiyao Wang, Xinyuan Fang, Gregory Hager

Adaptive Neural Network Based Control Approach for Building Energy Control Under Changing Environmental Conditions Lilli Frison, Simon Gölzhäuser

Adaptive Online Non-Stochastic Control Naram Mhaisen, George Iosifidis

Adaptive Teaching in Heterogeneous Agents: Balancing Surprise in Sparse Reward Scenarios Emma Clark, Kanghyun Ryu, Negar Mehr

An Efficient Data-Based Off-Policy Q-Learning Algorithm for Optimal Output Feedback Control of Linear Systems Mohammad Alsalti, Victor G. Lopez, Matthias A. Müller

An Invariant Information Geometric Method for High-Dimensional Online Optimization Zhengfei Zhang, Yunyue Wei, Yanan Sui

An Investigation of Time Reversal Symmetry in Reinforcement Learning Brett Barkley, Amy Zhang, David Fridovich-Keil

Balanced Reward-Inspired Reinforcement Learning for Autonomous Vehicle Racing Zhen Tian, Dezong Zhao, Zhihao Lin, David Flynn, Wenjing Zhao, Daxin Tian

Bounded Robustness in Reinforcement Learning via Lexicographic Objectives Daniel Jarne Ornia, Licio Romao, Lewis Hammond, Manuel Mazo Jr, Alessandro Abate

CACTO-SL: Using Sobolev Learning to Improve Continuous Actor-Critic with Trajectory Optimization Elisa Alboni, Gianluigi Grandesso, Gastone Pietro Rosati Papini, Justin Carpentier, Andrea Del Prete

Can a Transformer Represent a Kalman Filter? Gautam Goel, Peter Bartlett

Combining Model-Based Controller and ML Advice via Convex Reparameterization Junxuan Shen, Adam Wierman, Guannan Qu

Conditions for Parameter Unidentifiability of Linear ARX Systems for Enhancing Security Xiangyu Mao, Jianping He, Chengpu Yu, Chongrong Fang

Continual Learning of Multi-Modal Dynamics with External Memory Abdullah Akgül, Gozde Unal, Melih Kandemir

Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning Algorithms Xiangyuan Zhang, Weichao Mao, Saviz Mowlavi, Mouhacine Benosman, Tamer Başar

Convergence Guarantees for Adaptive Model Predictive Control with Kinky Inference Riccardo Zuliani, Raffaele Soloperto, John Lygeros

Convex Approximations for a Bi-Level Formulation of Data-Enabled Predictive Control Xu Shang, Yang Zheng

Convex Neural Network Synthesis for Robustness in the 1-Norm Ross Drummond, Chris Guiver, Matthew Turner

CoVO-MPC: Theoretical Analysis of Sampling-Based MPC and Optimal Covariance Design Zeji Yi, Chaoyi Pan, Guanqi He, Guannan Qu, Guanya Shi

Data Driven Verification of Positive Invariant Sets for Discrete, Nonlinear Systems Amy K. Strong, Leila J. Bridgeman

Data-Driven Bifurcation Analysis via Learning of Homeomorphism Wentao Tang

Data-Driven Robust Covariance Control for Uncertain Linear Systems Joshua Pilipovsky, Panagiotis Tsiotras

Data-Driven Simulator for Mechanical Circulatory Support with Domain Adversarial Neural Process Sophia Sun, Wenyuan Chen, Zihao Zhou, Sonia Fereidooni, Elise Jortberg, Rose Yu

Data-Driven Strategy Synthesis for Stochastic Systems with Unknown Nonlinear Disturbances Ibon Gracia, Dimitris Boskos, Luca Laurenti, Morteza Lahijanian

Data-Efficient, Explainable and Safe Box Manipulation: Illustrating the Advantages of Physical Priors in Model-Predictive Control Achkan Salehi, Stephane Doncieux

DC4L: Distribution Shift Recovery via Data-Driven Control for Deep Learning Models Vivian Lin, Kuk Jin Jang, Souradeep Dutta, Michele Caprio, Oleg Sokolsky, Insup Lee

Decision Boundary Learning for Safe Vision-Based Navigation via Hamilton-Jacobi Reachability Analysis and Support Vector Machine Tara Toufighi, Minh Bui, Rakesh Shrestha, Mo Chen

Deep Hankel Matrices with Random Elements Nathan Lawrence, Philip Loewen, Shuyuan Wang, Michael Forbes, Bhushan Gopaluni

Deep Model-Free KKL Observer: A Switching Approach Johan Peralez, Madiha Nadri

Design of Observer-Based Finite-Time Control for Inductively Coupled Power Transfer System with Random Gain Fluctuations Satheesh Thangavel, Sakthivel Rathinasamy

Distributed On-the-Fly Control of Multi-Agent Systems with Unknown Dynamics: Using Limited Data to Obtain Near-Optimal Control Shayan Meshkat Alsadat, Nasim Baharisangari, Zhe Xu

Do No Harm: A Counterfactual Approach to Safe Reinforcement Learning Sean Vaskov, Wilko Schwarting, Chris Baker

Dynamics Harmonic Analysis of Robotic Systems: Application in Data-Driven Koopman Modelling Daniel Ordoñez-Apraez, Vladimir Kostic, Giulio Turrisi, Pietro Novelli, Carlos Mastalli, Claudio Semini, Massimilano Pontil

Efficient Imitation Learning with Conservative World Models Victor Kolev, Rafael Rafailov, Kyle Hatch, Jiajun Wu, Chelsea Finn

Efficient Skill Acquisition for Insertion Tasks in Obstructed Environments Jun Yamada, Jack Collins, Ingmar Posner

Error Bounds, PL Condition, and Quadratic Growth for Weakly Convex Functions, and Linear Convergences of Proximal Point Methods Feng-Yi Liao, Lijun Ding, Yang Zheng

Event-Triggered Safe Bayesian Optimization on Quadcopters Antonia Holzapfel, Paul Brunzema, Sebastian Trimpe

Expert with Clustering: Hierarchical Online Preference Learning Framework Tianyue Zhou, Jung-Hoon Cho, Babak Rahimi Ardabili, Hamed Tabkhi, Cathy Wu

Finite-Time Complexity of Incremental Policy Gradient Methods for Solving Multi-Task Reinforcement Learning Yitao Bai, Thinh Doan

From Raw Data to Safety: Reducing Conservatism by Set Expansion Mohammad Bajelani, Klaske Van Heusden

Generalized Constraint for Probabilistic Safe Reinforcement Learning Weiqin Chen, Santiago Paternain

Global Rewards in Multi-Agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems Heiko Hoppe, Tobias Enders, Quentin Cappart, Maximilian Schiffer

Gradient Shaping for Multi-Constraint Safe Reinforcement Learning Yihang Yao, Zuxin Liu, Zhepeng Cen, Peide Huang, Tingnan Zhang, Wenhao Yu, Ding Zhao

Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution Tim Seyde, Peter Werner, Wilko Schwarting, Markus Wulfmeier, Daniela Rus

Hacking Predictors Means Hacking Cars: Using Sensitivity Analysis to Identify Trajectory Prediction Vulnerabilities for Autonomous Driving Security Marsalis Gibson, David Babazadeh, Claire Tomlin, Shankar Sastry

Hamiltonian GAN Christine Allen-Blanchette

How Safe Am I Given What I See? Calibrated Prediction of Safety Chances for Image-Controlled Autonomy Zhenjiang Mao, Carson Sobolewski, Ivan Ruchkin

HSVI-Based Online Minimax Strategies for Partially Observable Stochastic Games with Neural Perception Mechanisms Rui Yan, Gabriel Santos, Gethin Norman, David Parker, Marta Kwiatkowska

Improving Sample Efficiency of High Dimensional Bayesian Optimization with MCMC Zeji Yi, Yunyue Wei, Chu Xin Cheng, Kaibo He, Yanan Sui

In Vivo Learning-Based Control of Microbial Populations Density in Bioreactors Sara Maria Brancato, Davide Salzano, Francesco De Lellis, Davide Fiore, Giovanni Russo, Mario di Bernardo

Increasing Information for Model Predictive Control with Semi-Markov Decision Processes Rémy Hosseinkhan Boucher, Stella Douka, Onofrio Semeraro, Lionel Mathelin

Interpretable Data-Driven Model Predictive Control of Building Energy Systems Using SHAP Patrick Henkel, Tobias Kasperski, Phillip Stoffel, Dirk Müller

Inverse Optimal Control as an Errors-in-Variables Problem Rahel Rickenbach, Anna Scampicchio, Melanie N. Zeilinger

Lagrangian Inspired Polynomial Estimator for Black-Box Learning and Control of Underactuated Systems Giulio Giacomuzzo, Riccardo Cescon, Diego Romeres, Ruggero Carli, Alberto Dalla Libera

Learning “look-Ahead” Nonlocal Traffic Dynamics in a Ring Road Chenguang Zhao, Huan Yu

Learning $\epsilon$-Nash Equilibrium Stationary Policies in Stochastic Games with Unknown Independent Chains Using Online Mirror Descent Tiancheng Qin, S. Rasoul Etesami

Learning and Deploying Robust Locomotion Policies with Minimal Dynamics Randomization Luigi Campanaro, Siddhant Gangapurwala, Wolfgang Merkt, Ioannis Havoutis

Learning Flow Functions of Spiking Systems Miguel Aguiar, Amritam Das, Karl H. Johansson

Learning for CasADi: Data-Driven Models in Numerical Optimization Tim Salzmann, Jon Arrizabalaga, Joel Andersson, Marco Pavone, Markus Ryll

Learning Locally Interacting Discrete Dynamical Systems: Towards Data-Efficient and Scalable Prediction Beomseok Kang, Harshit Kumar, Minah Lee, Biswadeep Chakraborty, Saibal Mukhopadhyay

Learning Robust Policies for Uncertain Parametric Markov Decision Processes Luke Rickard, Alessandro Abate, Kostas Margellos

Learning Soft Constrained MPC Value Functions: Efficient MPC Design and Implementation Providing Stability and Safety Guarantees Nicolas Chatzikiriakos, Kim Peter Wabersich, Felix Berkel, Patricia Pauli, Andrea Iannelli

Learning to Stabilize High-Dimensional Unknown Systems Using Lyapunov-Guided Exploration Songyuan Zhang, Chuchu Fan

Learning True Objectives: Linear Algebraic Characterizations of Identifiability in Inverse Reinforcement Learning Mohamad Louai Shehab, Antoine Aspeel, Nikos Arechiga, Andrew Best, Necmiye Ozay

Learning-Based Rigid Tube Model Predictive Control Yulong Gao, Shuhao Yan, Jian Zhou, Mark Cannon, Alessandro Abate, Karl Henrik Johansson

Leveraging Hamilton-Jacobi PDEs with Time-Dependent Hamiltonians for Continual Scientific Machine Learning Paula Chen, Tingwei Meng, Zongren Zou, Jérôme Darbon, George Em Karniadakis

Linearised Data-Driven LSTM-Based Control of Multi-Input HVAC Systems Andreas Hinderyckx, Florence Guillaume

Mapping Back and Forth Between Model Predictive Control and Neural Networks Ross Drummond, Pablo Baldivieso, Giorgio Valmorbida

Meta-Learning Linear Quadratic Regulators: A Policy Gradient MAML Approach for Model-Free LQR Leonardo Felipe Toso, Donglin Zhan, James Anderson, Han Wang

Minimax Dual Control with Finite-Dimensional Information State Olle Kjellqvist

Mixing Classifiers to Alleviate the Accuracy-Robustness Trade-Off Yatong Bai, Brendon G. Anderson, Somayeh Sojoudi

MPC-Inspired Reinforcement Learning for Verifiable Model-Free Control Yiwen Lu, Zishuo Li, Yihan Zhou, Na Li, Yilin Mo

Multi-Agent Assignment via State Augmented Reinforcement Learning Leopoldo Agorio, Sean Van Alen, Miguel Calvo-Fullana, Santiago Paternain, Juan Andrés Bazerque

Multi-Agent Coverage Control with Transient Behavior Consideration Runyu Zhang, Haitong Ma, Na Li

Multi-Modal Conformal Prediction Regions by Optimizing Convex Shape Templates Renukanandan Tumu, Matthew Cleaveland, Rahul Mangharam, George Pappas, Lars Lindemann

Neural Operators for Boundary Stabilization of Stop-and-Go Traffic Yihuai Zhang, Ruiguo Zhong, Huan Yu

Neural Processes with Event Triggers for Fast Adaptation to Changes Paul Brunzema, Paul Kruse, Sebastian Trimpe

Nonasymptotic Regret Analysis of Adaptive Linear Quadratic Control with Model Misspecification Bruce Lee, Anders Rantzer, Nikolai Matni

Nonconvex Scenario Optimization for Data-Driven Reachability Elizabeth Dietrich, Alex Devonport, Murat Arcak

On Task-Relevant Loss Functions in Meta-Reinforcement Learning Jaeuk Shin, Giho Kim, Howon Lee, Joonho Han, Insoon Yang

On the Convergence of Adaptive First Order Methods: Proximal Gradient and Alternating Minimization Algorithms Puya Latafat, Andreas Themelis, Panagiotis Patrinos

On the Nonsmooth Geometry and Neural Approximation of the Optimal Value Function of Infinite-Horizon Pendulum Swing-up Haoyu Han, Heng Yang

On the Uniqueness of Solution for the Bellman Equation of LTL Objectives Zetong Xuan, Alper Bozkurt, Miroslav Pajic, Yu Wang

Online Decision Making with History-Average Dependent Costs Vijeth Hebbar, Cedric Langbort

Parameter-Adaptive Approximate MPC: Tuning Neural-Network Controllers Without Retraining Henrik Hose, Alexander Gräfe, Sebastian Trimpe

Parameterized Fast and Safe Tracking (FaSTrack) Using DeepReach Hyun Joe Jeong, Zheng Gong, Somil Bansal, Sylvia Herbert

PDE Control Gym: A Benchmark for Data-Driven Boundary Control of Partial Differential Equations Luke Bhan, Yuexin Bian, Miroslav Krstic, Yuanyuan Shi

Physically Consistent Modeling & Identification of Nonlinear Friction with Dissipative Gaussian Processes Rui Dai, Giulio Evangelisti, Sandra Hirche

Physics-Constrained Learning of PDE Systems with Uncertainty Quantified Port-Hamiltonian Models Kaiyuan Tan, Peilun Li, Thomas Beckers

Physics-Informed Neural Networks with Unknown Measurement Noise Philipp Pilar, Niklas Wahlström

Piecewise Regression via Mixed-Integer Programming for MPC Dieter Teichrib, Moritz Schulze Darup

PlanNetX: Learning an Efficient Neural Network Planner from MPC for Longitudinal Control Jasper Hoffmann, Diego Fernandez Clausen, Julien Brosseit, Julian Bernhard, Klemens Esterle, Moritz Werling, Michael Karg, Joschka Joschka Bödecker

Pointwise-in-Time Diagnostics for Reinforcement Learning During Training and Runtime Noel Brindise, Andres Posada Moreno, Cedric Langbort, Sebastian Trimpe

Pontryagin Neural Operator for Solving General-Sum Differential Games with Parametric State Constraints Lei Zhang, Mukesh Ghimire, Zhe Xu, Wenlong Zhang, Yi Ren

Probabilistic ODE Solvers for Integration Error-Aware Numerical Optimal Control Amon Lahr, Filip Tronarp, Nathanael Bosch, Jonathan Schmidt, Philipp Hennig, Melanie N. Zeilinger

Probably Approximately Correct Stability of Allocations in Uncertain Coalitional Games with Private Sampling George Pantazis, Filiberto Fele, Filippo Fabiani, Sergio Grammatico, Kostas Margellos

Proto-MPC: An Encoder-Prototype-Decoder Approach for Quadrotor Control in Challenging Winds Yuliang Gu, Sheng Cheng, Naira Hovakimyan

QCQP-Net: Reliably Learning Feasible Alternating Current Optimal Power Flow Solutions Under Constraints Sihan Zeng, Youngdae Kim, Yuxuan Ren, Kibaek Kim

Rademacher Complexity of Neural ODEs via Chen-Fliess Series Joshua Hanson, Maxim Raginsky

Random Features Approximation for Control-Affine Systems Kimia Kazemian, Yahya Sattar, Sarah Dean

Real-Time Safe Control of Neural Network Dynamic Models with Sound Approximation Hanjiang Hu, Jianglin Lan, Changliu Liu

Real-World Fluid Directed Rigid Body Control via Deep Reinforcement Learning Mohak Bhardwaj, Thomas Lampe, Michael Neunert, Francesco Romano, Abbas Abdolmaleki, Arunkumar Byravan, Markus Wulfmeier, Martin Riedmiller, Jonas Buchli

Recursively Feasible Shrinking-Horizon MPC in Dynamic Environments with Conformal Prediction Guarantees Charis Stamouli, Lars Lindemann, George Pappas

Reinforcement Learning-Driven Parametric Curve Fitting for Snake Robot Gait Design Jack Naish, Jacob Rodriguez, Jenny Zhang, Bryson Jones, Guglielmo Daddi, Andrew Orekhov, Rob Royce, Michael Paton, Howie Choset, Masahiro Ono, Rohan Thakker

Residual Learning and Context Encoding for Adaptive Offline-to-Online Reinforcement Learning Mohammadreza Nakhaei, Aidan Scannell, Joni Pajarinen

Restless Bandits with Rewards Generated by a Linear Gaussian Dynamical System Jonathan Gornet, Bruno Sinopoli

Robust Cooperative Multi-Agent Reinforcement Learning: A Mean-Field Type Game Perspective Muhammad Aneeq Uz Zaman, Mathieu Laurière, Alec Koppel, Tamer Başar

Robust Exploration with Adversary via Langevin Monte Carlo Hao-Lun Hsu, Miroslav Pajic

Safe Dynamic Pricing for Nonstationary Network Resource Allocation Berkay Turan, Spencer Hutchinson, Mahnoosh Alizadeh

Safe Learning in Nonlinear Model Predictive Control Johannes Buerger, Mark Cannon, Martin Doff-Sotta

Safe Online Convex Optimization with Multi-Point Feedback Spencer Hutchinson, Mahnoosh Alizadeh

Safety Filters for Black-Box Dynamical Systems by Learning Discriminating Hyperplanes Will Lavanakul, Jason Choi, Koushil Sreenath, Claire Tomlin

Signatures Meet Dynamic Programming: Generalizing Bellman Equations for Trajectory Following Motoya Ohnishi, Iretiayo Akinola, Jie Xu, Ajay Mandlekar, Fabio Ramos

Soft Convex Quantization: Revisiting Vector Quantization with Convex Optimization Tanmay Gautam, Reid Pryzant, Ziyi Yang, Chenguang Zhu, Somayeh Sojoudi

SpOiLer: Offline Reinforcement Learning Using Scaled Penalties Padmanaba Srinivasan, William J. Knottenbelt

Stable Modular Control via Contraction Theory for Reinforcement Learning Bing Song, Jean-Jacques Slotine, Quang-Cuong Pham

State-Wise Safe Reinforcement Learning with Pixel Observations Sinong Zhan, Yixuan Wang, Qingyuan Wu, Ruochen Jiao, Chao Huang, Qi Zhu

STEMFold: Stochastic Temporal Manifold for Multi-Agent Interactions in the Presence of Hidden Agents Hemant Kumawat, Biswadeep Chakraborty, Saibal Mukhopadhyay

Strengthened Stability Analysis of Discrete-Time Lurie Systems Involving ReLU Neural Networks Carl Richardson, Matthew Turner, Steve Gunn, Ross Drummond

Submodular Information Selection for Hypothesis Testing with Misclassification Penalties Jayanth Bhargav, Mahsa Ghasemi, Shreyas Sundaram

System-Level Safety Guard: Safe Tracking Control Through Uncertain Neural Network Dynamics Models Xiao Li, Yutong Li, Anouck Girard, Ilya Kolmanovsky

The Behavioral Toolbox Ivan Markovsky

Towards Bio-Inspired Control of Aerial Vehicle: Distributed Aerodynamic Parameters for State Prediction Yikang Wang, Adolfo Perrusquia, Dmitry Ignatyev

Towards Model-Free LQR Control over Rate-Limited Channels Aritra Mitra, Lintao Ye, Vijay Gupta

Towards Safe Multi-Task Bayesian Optimization Jannis Lübsen, Christian Hespe, Annika Eichler

Tracking Object Positions in Reinforcement Learning: A Metric for Keypoint Detection Emma Cramer, Jonas Reiher, Sebastian Trimpe

Uncertainty Informed Optimal Resource Allocation with Gaussian Process Based Bayesian Inference Samarth Gupta, Saurabh Amin

Uncertainty Quantification and Robustification of Model-Based Controllers Using Conformal Prediction Kong Yao Chee, Thales C. Silva, M. Ani Hsieh, George J. Pappas

Uncertainty Quantification of Set-Membership Estimation in Control and Perception: Revisiting the Minimum Enclosing Ellipsoid Yukai Tang, Jean-Bernard Lasserre, Heng Yang

Understanding the Difficulty of Solving Cauchy Problems with PINNs Tao Wang, Bo Zhao, Sicun Gao, Rose Yu

Verification of Neural Reachable Tubes via Scenario Optimization and Conformal Prediction Albert Lin, Somil Bansal

Wasserstein Distributionally Robust Regret-Optimal Control over Infinite-Horizon Taylan Kargin, Joudi Hajar, Vikrant Malik, Babak Hassibi